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One of the major problems in nutritional epidemiology is that we have a hard time measuring things that we are supposed to be measuring in order to say anything meaningful about relationships between diet and chronic disease. You know, things like how much people are actually eating or how much physical activity they really do get.

Today I had the opportunity to hear Walter Willett, king daddy of the field of nutritional epidemiology, speak on just this dilemma (and yes, he still has that sweet ‘stache). Introduced as having received “too many awards to mention,” W began his talk–“Energy Balance and Beyond: The Power and Limits of Dietary Data”–by addressing the recent unpleasantness raised by researchers who have suggested that the dietary data that we collect simply isn’t worth analyzing (Archer, Pavela, & Lavie 2015; Dhurandhar et al. 2015).

Having been recently immersed in my rhetoric of science readings, I noted that W started right off with some perfunctory boundary work, as way of indicating who was “in” and who was “out” when it came to credibility: He noted that the investigators questioning the value of dietary self-reports are “funded by Coca-cola,” and even the ones that are “pretty good scientists” are “not epidemiologists” and therefore “a little bit naive.” So much for evaluating the data and the arguments on their own merits.

Then he trotted out the “slippery slope” argument: If we throw out self-reported dietary data because it is wildly inaccurate, then not only do we have to “throw out the Dietary Guidelines” (heaven forfend!), but we’d have to throw out occupational safety and drug trial data also–because these are often based on self-reports. Certainly, there’s very little difference between reporting on events in your workplace or what side effects you might have in response to a pill you’re taking and reporting on what you remember eating over the course of the past year.

Then he got down to the nitty-gritty. The reason your Average American is fat is, to put it bluntly, because of math:

2500 kcals/day x 1% = 25 kcals/day

25 kcals/day x 365 days/year = 9125 kcals/year

9125 kcals/year = about 1kg of weight gain/year

Of course, the Average American only gains (on average) about 0.5 kg/year, but W easily explained the discrepancy: We gain weight, but then we have to expend more energy dragging our fat asses around–my words, not his–so we don’t gain as much weight as we would, but then the increased energy expenditure makes us hungry, so we eat more, so we gain weight, ad infinitum, only not, because we seem to plateau, but then, well, there’s that.

So here’s the $64,000 (or really a few hundred million in grant money) question: How good are we at measuring the 1% (or less) difference between “energy in” and “energy out” that we see expressed as weight gain in the population?

The answer, according to the man himself? Not very.

W then showed a comparison of a set of methods to measure “energy in,” along with their coefficient of variation (you can get the technical explanation here, but it is–simplistically–a measure of the amount of variability in your data; long story short: larger numbers = more variability, less precision and smaller numbers = less variability, more precision).

It looked something like this:

Method

Co-efficient of variation

Food frequency questionaire (FFQ)

15%

Diet record

13%

24-hour diet recall interview

28%

Doubly-labeled water (DLW)

9%

Weight

3%

What W made clear is that, if you want to know whether–or to what extent–Americans are indeed eating more and moving less, you can’t measure “energy in” using FFQs, diet records, and interviews and expect to get anywhere near that “1% of calories” accuracy you’re looking for. You can’t even rely on doubly-labeled water, typically considered to be a biomarker measurement with a high degree of precision. In fact, W made the point that DLW samples sent out to different labs would come back with results that differed by up to 50%.

W went on to explain that we have similar difficulties measuring “energy out,” with our very best measurements having a coefficient of variation of nearly 20%.

So in other words, or actually in W’s exact words:

“Weight is the best measure of energy balance.”

Wait? Weight?

As far as I can figure it, using weight as a way of “measuring” (and I use that term loosely) energy balance creates a theoretical–if not a methodological–situation that is, in a word, unfalsifiable. Or–in another word–bogus.

“Weight gain results when the things that we think cause weight gain happen, thus proving that those things have happened.”

At least one of the reasons for attempting to measure “energy in” and “energy out” is to find out whether or not it makes sense to attribute weight gain (or loss) to the differences between them. Instead, W is saying, we can know all we really need to know about how much people eat or move or both, because, voila, weight.

This is like saying, even though traces of tooth fairy fingerprints, footprints, or fiber samples are extremely difficult to obtain, we can reliably determine the existence of tooth fairies by the presence of quarters under your pillow.

Not only does this completely disregard the ever-growing list of things that may also contribute to weight gain/loss over time,* but it contradicts W’s own assertion just moments later that–even though we can’t do it very accurately–a good reason for attempting to measure total energy intake is that it can act as a “crude measure of physical activity.”

One the one hand, W is saying we can estimate how active you are by finding out how much you eat because people who eat more are also likely to be more active–that’s why they eat more–and people who eat less are typically less active–that’s why they don’t eat as much–with the implication being that “energy in” and “energy out” are positively related; as one changes, the other changes in the same direction.

On the other hand, he’s saying these two variables are completely independent of each other. Eating more doesn’t imply you move more; moving less doesn’t imply you eat less. And the way we know that these two variables are disconnected in any given Average American is, voila again, weight.

At this point, it was hard not to be completely distracted by the cognitive dissonance ringing through the room.

I did hang on long enough to hear W say that we shouldn’t really be worried about energy intake anyway because what really matters is diet quality, which, by the way, we can’t measure accurately either.

With all due respect, all I could think of is that while Emperor W may not be completely without clothes, he was definitely down to boxer shorts today.

In an auditorium full of really smart people, I cannot have been the only person thinking that W and his data looked a little over-exposed. But–as we saw with the circumstances revealed by the Ramsden and Zamora paper last week–it can be hard to contradict a famous colleague, and in nutritional epidemiology, no one is famouser than W. It may be even harder, I suppose, when he is an invited guest and an apparently nice fellow. The Q&A was respectful and polite. Difficult as it is to believe, even I kept my mouth shut.

I know science sometimes advances one funeral at a time, and I truly wish W a long and happy life. But maybe he’ll start to get a little chilly there in his boxers and start thinking about retiring someplace warm. Soon.

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In case you missed it, in a recent article published in the American Journal of Preventive Medicine entitled Overstatement of Results in the Nutrition and Obesity Peer-Reviewed Literature (not making this up), the authors found that a lot of papers published in the field of obesity and nutrition have, shall we say, issues.

What’s more, nutrition policy recommendations are supposed to be based on observational data. Hello? Dietary Guidelines? (Seriously. You don’t expect public health nutrition people to do actual experiments now, do you? I mean, unless you are talking about our population-wide, no-control-group, 35-year experiment with low-fat diet recommendations, but that’s different.)

And we don’t mind generalizing conclusions to Everyone in the Whole Wide World based on data from a bunch of white health care professionals born before the atom bomb because, honestly, those are the only data we really care about.

Equating correlation and causation, over-generalizing observations, and then using these results as the basis of policy is the bread (whole wheat) and butter (substitute) of nutrition epidemiology of chronic disease (aka NECD – pronounced Southern-style as “nekked”). NECD has a long proud tradition of misinterpreting results this way, and dammit, nobody is going to take that away from us.

Early NECD researchers have in the past tried to tentatively misinterpret results by obliquely implying that observed nutritional patterns might perhaps have resulted in the disease under investigation. Wusses.

In 1990, Walter Willett and JoAnn Manson came along to show us how the pros do it. These mavericks were the ones who made bold inroads into the kind of overreaching conclusions that made NECD great. Their data come from an observational study of female registered nurses from 11 states in the US, born between 1921 and 1946, who were asked to remember and report what they ate 4 whole times between 1976 and 1984, plus remember and report what they weighed when they were 18 years old. From this dataset, which is clearly comprehensive, and this population, which is practically every female in the US, Willett, Manson and company naturally conclude that “obesity is a major cause of excess morbidity and mortality from coronary heart disease among women in the United States” (emphasis mine). None of this wimpy “associated with increased risk of” bullshooey, obesity CAUSES heart disease, they tell us, CAUSES IT!!!! BWHAAAHAAAAA!!!!!!!

It is on this foundation of intrepid willingness to misinterpret data that the science of NECD was built. This is why Walter Willett is the Big Kahuna at the Harvard School of Public Health. He has demonstrated the courage to misinterpret data in innovative and comprehensive ways, publishing articles throughout his career that indicate that even small increases in BMI—including BMI levels that are currently considered “normal”–cause chronic disease.

In 1999, in what is considered a landmark article in overstatement, one with which all NECD acolytes should familiarize themselves, he states unequivocally, in a review of observational data:

The rest of that sentence reads: ” . . . and is a growing problem in many countries.” His data is once again gathered mostly from American white health care professionals born before the atom bomb. Generalization from specific populations to the rest of the world? Ding ding.

And what should we do with this conclusion, according to Willett? “Preventing weight gain and overweight among persons with healthy weights and avoiding further weight gain among those already overweight are important public health goals.” Using observed associations to make policy recommendations? Ding ding ding. In one fell swoop, Willett dexterously manages to use all three designated methods of overstatement and misinterpretation in the nutrition epidemiology NECD toolbox, demonstrating why he is considered by most researchers to be “the ‘father’ of nutrition epidemiology.” This man overstates and misinterprets in ways that the rest of us can only dream of doing.

But that’s okay. Willett and the Harvard Family know how to deal with this sort of thing.

“Someday, and that day appears to have come, I will call upon you to ignore the work of other scientists when their results contradict my own.”

Let’s face it, in the world of NECD, you can’t just have people like Flegal refusing to infer causation from observed results, just because they don’t want to. When that sort of thing happens, well, let’s just say, if she won’t do it, the Harvard Family will have to do it for her. And so they did.

The Family get-together was held at the Harvard School of Public Health, a “neutral convening space” that is also ground zero for the Nurses’ Health Study I and II, the Physicians Health Study I and II, and the Health Professional Follow Up Study, three datasets that have generated many NECD articles that, unlike Flegal’s article, brilliantly illustrate the powers of misinterpreting observational data. That Flegal herself was invited, but “could not attend” tells us just how ashamed she must be of her inability to make over-reaching conclusions–or perhaps she was temporarily “incapacitated” if you know what I mean.

The webcast from the meeting show us how NECD should be done, with dazzling examples of overstatement and marvelous feats of misinterpretation.

In the world of NECD, PowerPoint arrows are a scientifically-acceptable method of establishing causation.

The panelists highlighted the importance of maintaining clear standards of overstatement and expressed concern that Flegal’s research could undermine future attempts of more credible researchers to misinterpret data as needed to protect the health of the public.

Because that’s what it’s all about folks: protection. Someone needs to protect the science from renegades like Flegal, and someone needs to protect the public from science.

We should be thankful that we have Willett and the Harvard Family there. They know that data like Flegal’s can only confuse the poor widdle brains of Americans. Allowing us to be exposed to such “rubbish” might lead us to the risky conclusion that perhaps overweight and mild obesity won’t cause all of us to die badly, or to the even more dangerous notion that observational data should remark only upon association, not causation. And we sure don’t want that to happen.

We learned that there were some significant changes in those 40 years. We saw dramatic increases in vegetable oils, grain products, and poultry—the things that the 1977 Dietary Goals and the 1980 Dietary Guidelines told us to increase. We saw decreases in red meat, eggs, butter, and full-fat milk—things that our national dietary recommendations told us to decrease. Mysteriously, what didn’t seem to increase much—or at all—were SoFAS (meaning “Solid Fats and Added Sugars”) which, as far as the 2010 Dietary Guidelines for Americans are concerned, are the primary culprits behind our current health crisis. (“Solid Fats” are a linguistic sleight-of-hand that lumps saturated fat from natural animal sources in with processed partially-hydrogenated vegetables oils and margarines that contain transfats; SoFAS takes the trick a step further, by being not only a dreadful acronym in terms of implying that poor health is caused by sitting on our “sofas,” but by creating an umbrella term for foods that have little in common in terms of structure, biological function or nutrition.)

Around the late 70s or early 80s, there were sudden and rapid changes in America’s food supply and food choices and similar sudden and rapid changes in our health. How these two phenomena are related remains a matter of debate. It doesn’t matter if you’re Marion Nestle and you think the problem is calories or if you’re Gary Taubes and you think the problem is carbohydrate—both of those things increased in our food supply. (Whether or not the problem is fat is an open debate; food availability data points to an increase in added fats and oil, the majority of which are, ironically enough, the “healthy” monounsaturated kind; consumption data points to a leveling off of overall fat intake and a decrease in saturated fat—not a discrepancy I can solve here.) What seems to continue to mystify people is why this changed occurred so rapidly at this specific point in our food and health history.

Personally responsible or helplessly victimized?

At one time, it was commonly thought that obesity was a matter of personal responsibility and that our collective sense of willpower took a nosedive in the 80s, but nobody could ever explain quite why. (Perhaps a giant funk swept over the nation after The Muppet Show got cancelled, and we all collectively decided to console ourselves with Little Debbie Snack Cakes and Nickelodeon?) But because this approach is essentially industry-friendly (Hey, says Big Food, we just make the stuff!) and because no one has any explanation for why nearly three-quarters of our population decided to become fat lazy gluttons all at once (my Muppet Show theory notwithstanding) or for the increase of obesity among preschool children (clearly not affected by the Muppet Show’s cancellation), public health pundits and media-appointed experts have decided that obesity is no longer a matter of personal responsibility. Instead the problem is our “obesogenic environment,” created by the Big Bad Fast Processed Fatty Salty Sugary Food Industry.

Even though it is usually understood that a balance between supply and demand creates what happens in the marketplace, Michael Pollan has argued that it is the food industry’s creation of cheap, highly-processed, nutritionally-bogus food that has caused the rapid rise in obesity. If you are a fan of Pollanomics, it seems obvious that food industry—on a whim?—made a bunch of cheap tasty food, laden with fatsugarsalt, hoping that Americans would come along and eat it. And whaddaya know? They did! Sort of like a Field of Dreams only with Taco-flavored Doritos.

As a result, obesity has become a major public health problem.

Just like it was in 1952.

Helen Lee in thought-provoking article, The Making of the Obesity Epidemic (it is even longer than one of my blog posts, but well worth the time) describes how our obesity problem looked then:

“It is clear that weight control is a major public health problem,” Dr. Lester Breslow, a leading researcher, warned at the annual meeting of the western branch of the American Public Health Association (APHA). At the national meeting of the APHA later that year, experts called obesity “America’s No. 1 health problem.”

The year was 1952. There was exactly one McDonald’s in all of America, an entire six-pack of Coca-Cola contained fewer ounces of soda than a single Super Big Gulp today, and less than 10 percent of the population was obese.

In the three decades that followed, the number of McDonald’s restaurants would rise to nearly 8,000 in 32 countries around the world,sales of soda pop and junk food would explode — and yet, against the fears and predictions of public health experts, obesity in the United States hardly budged. The adult obesity rate was 13.4 percent in 1960. In 1980, it was 15 percent. If fast food was making us fatter, it wasn’t by very much.

Then, somewhat inexplicably, obesity took off.”

It is this “somewhat inexplicably” that has me awake at night gnashing my teeth.

And what is Government going to do about it?

I wonder how “inexplicable” it would be to Ms. Lee had she put these two things together:

(In case certain peoples have trouble with this concept, I’ll type this very slowly and loudly: I’m not implying that the Dietary Guidelines “caused” the rise in obesity; I am merely illustrating a temporal relationship of interest to me, and perhaps to a few billion other folks. I am also not implying that a particular change in diet “caused” the rise in obesity. My focus is on the widespread and encompassing effects that may have resulted from creating one official definition of “healthy food choices to prevent chronic disease” for the entire population.)

In a similar fashion, the 1977 Dietary Goals were the culmination of concerns about obesity that had begun decades before, joined by concerns about heart disease voiced by a vocal minority of scientists led by Ancel Keys. Declines in red meat, butter, whole milk and egg consumption had already begun in response to fears about cholesterol and saturated fat that originated with Keys and the American Heart Association—which used fear of fat and the heart attacks they supposedly caused as a fundraising tactic, especially among businessmen and health professionals, whom they portrayed as especially susceptible to this disease of “successful civilization and high living.” The escalation of these fears—and declines in intake of animal foods portrayed as especially dangerous—picked up momentum when Senator George McGovern and his Select Senate Committee created the 1977 Dietary Goals for Americans. It was thought that, just as we had “tackled” smoking, we could create a document advising Americans on healthy food choices and compliance would follow. But issue was a lot less straightforward.

To begin with, when smoking was at its peak, only around 40% of the population smoked. On the other hand, we expect that approximately 100% of the population eats.

In addition, the anti-smoking campaigns of the 1960s and 1970s built on a long tradition of public health messages—originating with the Temperance movement—that associated smoking with dirty habits, loose living, and moral decay. It was going to be much harder to fully convince Americans that traditional foods typically associated with robust good health, foods that the US government thought were so nutritionally important that in the recent past they had been “saved” for the troops, were now suspect and to be avoided.

Where the American public had once been told to save “wheat, meat, and fats” for the soldiers, they now had to be convinced to separate the “wheat” from the “meat and fats” and believe that one was okay and the others were not.

To do this, public health leaders and policy makers turned to science, hoping to use it just as it had been used in anti-smoking arguments. Frankly, however, nutrition science just wasn’t up to the task. Linking nutrition to chronic disease was a field of study that would be in its infancy after it grew up a bit; in 1977, it was barely embryonic. There was little definitive data to support the notion that saturated fat from whole animal foods was actually a health risk; even experts who thought that the theory that saturated fat might be linked to heart disease had merit didn’t think there was enough evidence to call for dramatic changes in American’s eating habits.

The scientists who were intent on waving the “fear of fat” flag had to rely on observational studies of populations (considered then and now to be the weakest form of evidence), in order to attempt to prove that heart disease was related to intake of saturated fat (upon closer examination, these studies did not even do that).

Nutrition epidemiology is a soft science, so soft that it is not difficult to shape it into whatever conclusions the Consistent Public Health Message requires. In large-scale observational studies, dietary habits are difficult to measure and the results of Food Frequency Questionnaires are often more a product of wishful thinking than of reality. Furthermore, the size of associations in nutrition epidemiological studies is typically small—an order of magnitude smaller than those found for smoking and risk of chronic disease.

But nutrition epidemiology had proved its utility in convincing the public of the benefits of dietary change in the 70s and since then has become the primary tool—and the biggest funding stream (this is hardly coincidental)—for cementing in place the Consistent Public Health Message to reduce saturated fat and increase grains and cereals.

There is no doubt that the dramatic dietary change that the federal government was recommending was going to require some changes from the food industry, and they appear to have responded to the increased demands for low-fat,whole grain products with enthusiasm. Public health recommendations and the food fears they engendered are (as my friend James Woodward puts it) “a mechanism for encouraging consumers to make healthy eating decisions, with the ultimate goal of improving health outcomes.” Experts like Kelly Brownell and Marion Nestle decry the tactics used by the food industry of taking food components thought to be “bad” out of products while adding in components thought to be “good,” but it was federal dietary recommendations focusing above all else on avoiding saturated fat, cholesterol, and salt that led the way for such products to be marketed as “healthy” and to become acceptable to a confused, busy, and anxious public. The result was a decrease in demand for red meat, butter, whole milk and egg, and an increase in demand for low-saturated fat, low-cholesterol, and “whole” grain products. Minimally-processed animal-based products were replaced by cheaply-made, highly-processed plant-based products, which food manufacturers could market as healthy because, according to our USDA/HHS Dietary Guidelines, they were healthy.

The problem lies in the fact that—although these products contained less of the “unhealthy” stuff Americans were supposed to avoid—they also contained less of our most important nutrients, especially protein and fat-soluble vitamins. We were less likely to feel full and satisfied eating these products, and we were more likely to snack or binge—behaviors that were also fully endorsed by the food industry.

Between food industry marketing and the steady drumbeat of media messages explaining just how deadly red meat and eggs are (courtesy of population studies from Harvard, see above), Americans got the message. About 36% of the population believe that UFOs are real; only 25% believe that there’s no link between saturated fat and heart disease. We are more willing to believe that we’ve been visited by creatures from outer space than we are to believe that foods that humans have been eating ever since they became human have no harmful effects on health. But while industry has certainly taken advantage of our gullibility, they weren’t the ones who started those rumors, and they should not be shouldering all of the blame for the consequences.

Fixing it until it broke

Back in 1977, we were given a cure that didn’t work for diseases that we didn’t have. Then we spent billions in research dollars trying to get the glass slipper to fit the ugly stepsister’s foot. In the meantime, the food industry has done just what we would expect it to do, provide us with the foods that we think we should eat to be healthy and—when we feel deprived (because we are deprived)—with the foods we are hungry for.

We can blame industry, but as long as food manufacturers can take any mixture of vegetable oils and grain/cereals and tweak it with added fiber, vitamins, minerals, a little soy protein or maybe some chicken parts, some artificial sweeteners and salt substitutes, plus whatever other colors/preservatives/stabilizers/flavorizers they can get away with and still be able to get the right profile on the nutrition facts panel (which people do read), consumers–confused, busy, hungry–are going to be duped into believing what they are purchasing is “healthy” because–in fact–the government has deemed it so. And when these consumers are hungry later—which they are very likely to be—and they exercise their rights as consumers rather than their willpower, who should we blame then?

There is no way around it. Our dietary recommendations are at the heart of the problem they were created to try to reverse. Unlike the public health approach to smoking, we “fixed” obesity until it broke for real.

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Move over saturated fat and cholesterol. There’s a new kid on the heart disease block: TMAO.

TMAO is not, as I first suspected, a new internet acronym that I was going to have to get my kids to decipher for me, while they snickered under their collective breaths. Rather, TMAO stands for Trimethylamine N-oxide, and it is set to become the reigning king of the “why meat is bad for you” argument. Former contenders, cholesterol and saturated fat, have apparently lost their mojo. After years of dominating the heart disease-diet debate, it turns out they were mere poseurs, only pretending to cause heart disease, the whole time distracting us from the true evils of TMAO.

The news is, the cholesterol and saturated fat in red meat can no longer be held responsible for clogging up your arteries. TMAO, which is produced by gut bacteria that digest the carnitine found in meat, is going to gum them up instead. This may be difficult to believe, especially in light of the fact that, while red meat intake has declined precipitously in the past 40 years, prevalence of heart disease has continued to climb. However, this is easily accounted for by the increase in consumption of Red Bull—which also contains carnitine—even though it is not, as some may suspect, made from real bulls (thank you, BW).

Here to explain once again why we should all be afraid of eating a food our ancestors ignorantly consumed in scandalous quantities (see what happened to them? they are mostly dead!) is the Medical Media Circus! Ringleader for today is the New York Times’ Gina Kolata, who never met a half-baked nutrition theory she didn’t like (apparently Gary Taubes’ theory regarding carbohydrates was not half-baked enough for her).

Step right up folks and meet TMAO, the star of “a surprising new explanation of why red meat may contribute to heart disease” (because, frankly, the old explanations aren’t looking too good these days).

We know that red meat maybe almost probably for sure contributes to heart disease, because that wild bunch at Harvard just keeps cranking out studies like this one, Eat Red Meat and You Will Die Soon.

This study and others just like it definitely prove that if you are a white, well-educated, middle/upper-middle class health professional born between 1920 and 1946 and you smoke and drink, but you don’t exercise, watch your weight, or take a multivitamin, then eating red meat will maybe almost probably for sure increase your risk of heart disease. With evidence like that, who needs evidence?

Flying like the Wallenda family in the face of decades of concrete and well-proven assumptions that the reason we should avoid red meat is because of its saturated fat and cholesterol content, the daring young scientists who discovered the relationship between TMAO and heart disease “suspected that saturated fat and cholesterol made only a minor contribution to the increased amount of heart disease seen in red-meat eaters” [meaning that is, the red-meat eaters that are white, well-educated, middle/upper-middle class health professionals, who smoke and drink and don’t exercise, watch their weight, or take a multivitamin; emphasis mine].

Perhaps their suspicions were alerted by studies such as this one, that found that, in randomized, controlled trials, with over 65 thousand participants, people who reduced or changed their dietary fat intake didn’t actually live any longer than the people who just kept eating and enjoying the same artery-clogging, saturated fat- and cholesterol-laden foods that they always had. (However, this research was able to determine that a steady diet of broiled chicken breasts does in fact make the years crawl by more slowly.)

Exactly how TMAO increases the risk of heart disease, nobody knows. But, good scientists that they are, the scientists have a theory. (Just to clarify, in some situations the word theory means: a coherent group of tested general propositions, commonly regarded as correct. This is not one of those situations.) The researcher’s think that TMAO enables cholesterol to “get into” artery walls and prevents the body from excreting “excess” cholesterol. At least that’s how it works in mice. Although mice don’t normally eat red meat, it should be noted that mice are exactly like people except they don’t have Twitter accounts. We know this because earlier mouse studies allowed scientists to prove beyond the shadow of a doubt that dietary cholesterol and saturated fat cause heart disease mice definitely do not have Twitter accounts.

Look, just because the scientists can’t explain how TMAO does all the bad stuff it does, doesn’t mean it’s not in there doing, you know, bad stuff. Remember, we are talking about molecules that are VERY VERY small and really small things can be hard to find–unless of course you are on a scientific fishing expedition.

What will happen to the American Heart Association’s seal of approval now that saturated fat and cholesterol are no longer to be feared?

Frankly, I’m relieved that we FINALLY know exactly what has been causing all this heart disease. Okay, so it’s not the saturated fat and cholesterol that we’ve been avoiding for 35 years. Heck, everybody makes mistakes. Even though Frank Sacks and Robert Eckel, two scientists from the American Heart Association, told us for decades that eating saturated fat and cholesterol was just greasing the rails on the fast track to death-by-clogged-arteries, they have no reason to doubt this new theory. And even though they apparently had no reason to doubt the now-doubtful old theory, at least not until just now—as a nation, we can rest assured that THIS time, they got it right.

Now that saturated fat and cholesterol are no longer Public Enemies Number One and Two, whole milk, cheese, eggs, and butter—which do not contain red meat—MUST BE OKAY! I guess there’s no more need for the AHA’s dietary limits on saturated fat, or for the USDA Guidelines restrictions on cholesterol intake, or for those new Front of Package labels identifying foods with too much saturated fat. Schools can start serving whole milk again, butter will once again be legal in California, and fat-free cheese can go back to being the substance that mouse pads are made out of. Halla-freaking- looyah! A new day has dawned.

But—amidst the rejoicing–don’t forget: Whether we blame saturated fat or cholesterol or TMAO, meat is exactly as bad for you now as it was 50 years ago.

1. the n of 1 view: what works for you is what works, this is all that matters, end of story.

2. the Platonic view: this is how your body/metabolism works, and so this is what you should do and if it isn’t working you probably are not doing right.

I think many of us start off being interested in nutrition because we like to know stuff, and knowing stuff about how to be healthy and fit is really cool because then you get to look better in your bathing suit than most or you can solve health problems that others can’t or any number of other minor acts of smug superiority masquerading as an objective search for knowledge. When we start out, we usually are completely immersed in perspective #2, that there is a “right” way to eat and exercise. We figure out what the “right” way is through various forms of scientific investigation/reporting brought to us by experts and/or the media; we apply that magic formula to ourselves, and we wait for the magic results to happen. If we are young and unencumbered by reality, they usually do—no matter what formula for fitness and health we’ve chosen from the ones offered by the experts—and we congratulate ourselves for our hard work and strength of character.

For many of us, somewhere along the line, the magic formula stops working, or we stop working at the magic formula, or a little (or a lot) of both.

Some of us respond to this by looking for the next—better, easier, quicker, more doable—magic formula. Some of us respond by working even harder at the magic formula we haven’t given up on—yet. Some of us give up looking and trying because life is hard enough already.

But that doesn’t mean we’ve given up on the idea that there is a “right” way to go about being healthy. I was a low-fat vegetarian eater for 16 years because I thought it was the “right” way to eat. I’ve been a (mostly) low-carb, animal eater for 13 years, during most of which I thought I’d—finally—found the really “right” way to eat.

What I’d really found was a new and different way to be wrong.

I wasn’t wrong about the diet plan–for me. It helped me lose 60 pounds that I’ve kept off for 13 years without hunger, without a calculator, and without having to exercise more than I want to. What I was wrong about was being right. I was wrong about the magic formula—any magic formula.

[In blog posts yet to come, I’ll tell you all the story of the woman who changed my perspective on everything.]

I hate being wrong (although goodness knows I’m really good at it, from years of practice). I really want there to be a formula, magic or otherwise. I like order, routine, facts, and answers. Gray areas make me woozy. That’s why I love biochemistry. It’s a game with nothing but rules that, literally, every body has to follow.

But, to quote Andrew Abrahams again, a detailed understanding of the minutiae of biochemical mechanisms doesn’t really help us in the big messy world of real people. Although everyone is subject to the same biochemical rules, how those rules play out in any given individual is difficult—perhaps impossible—to predict.

I salute the work that Gary Taubes and Peter Attia are doing with NuSI, which will focus on providing randomized controlled experimental evidence regarding nutritional interventions. The idea is to have both highly controlled experiments and more “real world” ones. Hooray for both. These experiments may help us understand how well certain nutrition interventions work—in experimental situations with a selected group of individuals. As awesome as this might be for a scientific pursuit, this science still may not be of much help for you personally, depending on how closely matched you feel your life and your self are to the experimental conditions—and it won’t provide any easy answers for the hardest issue of all, public health policy.

One big long experiment

Is there a way to round up our messy, individual realities into comprehensible information that will eventually translate into meaningful policy? Maybe. Andrew Abrahams and others in the ancestral health community have been tossing around the idea of “n of 1” nutrition for a while. The basis for this approach is the idea that we all experiment. In fact, life is one big long experiment.

But how do we conduct these “n of 1” experiments in a manner that

helps the person doing the experiment learn the right lessons (rather than be distracted by coincidences or random events)?

As Andrew says, and I agree, individual characteristics, circumstances, and history are tremendously important as far as choosing food and activity that works for you. His idea is to create a way to help people with this n of 1 experimentation so they can evaluate how their body will respond to changes and find what’s right for them.

The purpose of this community would be to capture the wide variety of attributes that may contribute to the outcomes for any individual, and provide modeling tools that can help people make the right decisions about what changes to make.

From a participant’s perspective, it would:

provide a way for you to observe and analyze personal health in an organized and (more or less) objective fashion

give direction, support, and structure to your own n of 1 experimentation

create a community of fellow experimenters with whom you could compare/contrast results

From a health professional’s perspective, it would:

provide a way to assist clients/patients in find what works best for them without a superimposing “it’s supposed to work this way for everyone” bias

create a set of algorithms for adapting common patterns to individualized recommendations and further experimentation

For example: A postmenopausal female who wants to lose weight may start one way and experiment in a series of steps that is different from, say, a 30-year old marathoner who wants to have a healthy pregnancy.

From a researcher’s perspective, it would:

create a way to structure and conduct experiments across a variety of nutritional (and other) factors

allow sharing and analysis of both pooled results and case studies/series of relevant community members or subpopulations with common characteristics

develop tools allowing one to interpret the community results in an individual context, make predictions and suggest “next steps”

contribute to the development of modeling systems for complex and interrelated inputs and outputs

A different question means a different approach to public health

I see the value of n=1 as a scientific pursuit because it will teach us to ask a very different question than the one we’ve been asking. We’ve been asking, “What way of eating will prevent chronic disease in most/all Americans?” Typically, nutrition epidemiology is recruited to try to answer that question with the idea that there is some factor or factors (like smoking and lung cancer) that can be included/eliminated to reach this goal. We’ve been so phenomenally unsuccessful at chronic disease prevention with our current population-wide model that I think a new framework of investigation is needed. Thus, n of 1 investigation changes the question to something more like: “What way of eating will bring improved health to you now?”

As people make incremental changes toward shorter-term personal health goals, modeling tools can be used to map out “nearest neighbor” communities. These communities may be similar in terms of personal characteristics and health history, but also attributes relating to culture, region, lifestyle, ethnic and family background, education, income, etc. Over time, this information will reflect long-term health outcomes built on a background of complex human traits interacting with complex human environments.

The complexity of n of 1 nutrition seems to be the very opposite of public health nutrition. And it would be naïve to think that the concept of n of 1 will not be at least partially co-opted by the food, drug, and research industries (“Try new Methylation Carbonation –for PEMT polymorphisms!”). But by its very nature, n of 1 nutrition resists being turned into yet another “magic formula.” More importantly, it reframes our current approach to public health nutrition along two very important lines:

First, it weakens the current public health message that a one-size-fits-all dietary recommendation is appropriate. This is especially important because it has been assumed for 30+ years that dietary recommendations that are normed on one population are equally applicable to other populations. A landmark study published in 2010 shows that African-Americans who consumed a “healthier” diet according to Dietary Guidelines standards actually gained more weight over time than African-Americans who ate a “less healthy” diet [1].

DQI stands for Diet Quality Index. Blacks with a higher DQI had more weight gain over time than blacks with a lower DQI. From [1]

Second, n of 1 nutrition emphasizes the need to return to a focus on the provision of basic nutritional needs rather than prevention of chronic disease. Balancing the complexity of the n of 1 concept (i.e. each human is radically different from another) with the simplicity of promoting/understanding essential nutrition (i.e. but each human shares these same basic needs provided by food) moves us away from the prevention model to the provision model. And the literature is pretty straightforward about what our basic nutritional needs are:

essential amino acids

essential fatty acids

vitamins and minerals

sufficient energy

Notice anything missing on that list of essentials? As the Institute of Medicine’s Food and Nutrition Board says: “The lower limit of dietary carbohydrate compatible with life is apparently zero” (DRI, Ch. 6, 275) [2]. This doesn’t mean you can’t or shouldn’t eat carbohydrate foods, or that some carbohydrate foods aren’t beneficial for some people or even many people. Indeed, some of my best friends are carbs. But dietary carbohydrate is not an essential component of our nutritional needs and never has been (although it is a fine source of energy if energy is what is you need and you aren’t wearing a 6-month supply on your backside like I am). Rather, carbohydrate has been recommended as the source of the majority of our calories as a means of replacing the fat, saturated fat, and cholesterol that we’ve been told cause chronic disease.* This recommendation seems to have conveniently upsized the market for the industrialized and heavily marketed foods—made mostly from corn, wheat, and soy—that take up most of the space on our grocery store shelves.

But I think the most significant ramification of the history of our Dietary Guidelines is not its effect on diet so much as the acceptance of the notion that something as intimately and intricately related to our health, culture, personality, lifestyle, family, and history as food can and should be directed—in a most comprehensive manner—from a place exceedingly remote from the places where we actually get fed.

Focus on community

While the ostensible focus of n of 1 nutrition is the individual, the real focus is the community. Advances in both biological and social sciences are increasingly focused on what are now considered to be the primary determinants of health status for an individual: that person’s genetic community and that person’s present community. What health behaviors you as an individual think you “choose” have already been largely determined by social factors: culture, socioeconomic status, education, etc. Those behaviors interact with genetic and epigenetic mechanisms that you didn’t have much choice about either. Although every individual has some control over his/her health behaviors, many of the health outcomes that we think of as being a result of “individual choice” are already largely predetermined.

One of the enduring myths of healthcare in the US is that there are some folks out there who “choose” poor health. Maybe there are, but I’ve met a lot of people in poor health, and I’ve never met anyone who deliberately chose it.

As we find virtual “nearest neighbor” communities in our n of 1 nutrition database, we may be able to use this information to assist real communities to develop their own appropriate food-health systems. Despite our increasing diversity, much of America still clusters itself in communities that reflect shared characteristics which play leading roles in health and health behavior. Culturally-influenced food preferences and nutrition beliefs may be part of that community formation and/or may reinforce those communities. With scientific tools that embrace complexity and diversity, we can honor those characteristics that make one community (real or virtual) different from the next, rather than ignore them.

N of 1 nutritional approaches will give us a new way to think about public health nutrition and the individuals and communities most affected by nutrition policy. I’m proud to say that Healthy Nation Coalition will be supporting the project.

Up next: My take on why nutrition is a feminist issue, or “I am Woman, hear my stomach growl.”

*While on a field trip to Washington, DC in January of 2010, I met Linda Meyers, one of the authors of reference #2 below. I asked her why carbohydrates were recommended as such a large part of our diet if there is no essential requirement for them. Her response was that the recommendation was based on prevention of chronic disease. I’m still not sure I get that.

Nutritional epidemiology has many shortcomings when it comes to acting as a basis for public health nutrition policy. But you don’t have to take Walter Willett’s word for it. Apart from the weaknesses in the methodology, there is one great big elephant in the nutrition epidemiology room that no one really wants to talk about: our current culture-wide “health prescription.”

You don’t have to care about or read about nutrition to know that “fat is bad” and “whole grains are good” [1,2]. Whether or not you follow the nutrition part of the current “health prescription” is likely to depend on a host of other factors related to general “health prescription” adherence, which in turn may have a much larger impact on your health than your actual nutritional choices. This is especially true because variation in intake and/or variation in risk related to intake are frequently quite small.

For example, in a study relating French fry consumption to type 2 diabetes, the women who ate the least amount of French fries ate 0 servings per day while the women who ate the most ate 0.14 servings per day or about 5 French fries per day (i.e. not a big difference in intake) [3]. The risk of developing type 2 diabetes among 5-fries a day piggies was observed to be .21 times greater than the risk among the no-fry zone ladies (i.e. not a big variation in risk).

Okay, everyone knows that French fries are “bad for you.” But these ladies ate them anyway. Were there other factors related to general “health prescription” adherence which may have had an impact on their risk of diabetes?

The French fry eaters also “tended to have a higher dietary glycemic load and higher intakes of red meat, refined grain, and total calories. They were more likely to smoke but were less likely to take multivitamins and postmenopausal hormone therapy.” (They also exercised less.) In other words, the French fry eaters, within a context of a known “health prescription” had chosen to ignore a number of healthy lifestyle recommendations, not just the ones related to French fries.

If you think of our current default diet recommendation as the “placebo” (although its effects may not be exactly benign), it is clear that people who fail to comply with dietary prohibitions against red meat, saturated fats, and “junk” food like French fries may also be more likely to have other poor self-care habits, like smoking and not exercising. That poor health care habits are related to poor health is of no surprise to anyone.

Statistical people

In their statistical manipulation of a dataset, nutritional epidemiologists attempt to “control” for confounding variables (confounders), such as differences in health behavior. A confounder is something that may be related to both the hypothesized cause under investigation (i.e. French fry eating) and the outcome (i.e. type 2 diabetes). As such, it muddies the water when you are trying to figure out exactly what causes what.

When statisticians “control” or “adjust” for these confounders in a data set, they essentially “pretend” (that’s the exact word my biostats professor used) that the other qualities that any given individual brings to a data set are now equalized and that the specific factor under investigation—diet—has been isolated. Well, it has and it hasn’t. The “statistical humans” created by computer programs that now have equalized risk factors are a mirage; these people do not exist. The people who contributed the data that ostensibly demonstrates that “French fries increase risk of type 2 diabetes” are the exact same people who had other behaviors that may also contribute to increased risk of diabetes. (Please note: I chose this example, rather than “red meat causes heart disease” because there are many plausible explanations for French fries causing type 2 diabetes, it is just that you aren’t going to find evidence for them using this approach.)

Most nutritional epidemiology articles contain some version the following statement in their conclusions:

“We cannot rule out the possibility of unknown or residual confounding.”

Meaning: We can not rule out the possibility that our results can be explained by factors that we failed to fully take into account. Like the elephant in the room.

That this is actually the case becomes apparent when hypotheses that seem iron-clad in observational studies are put to the test in experimental conditions.

Lack of experimental confirmation

If ever there was a field about which you could say “for every study there is an equal and opposite study,” it is nutritional epidemiology–although experimental results are generally considered “more equal” than observational data. Associations that link specific nutrients to the prevention of specific diseases can be (relatively) strong and consistent in the context of nutritional epidemiology observational data, but absent in experimental situations. Epidemiological studies suggested that beta carotene could prevent cancer; experimental evidence suggested just the opposite and in fact, smokers given beta carotene supplements had increased risk of cancer [6]. Epidemiological studies suggest that low-fat, high-carb diets are related to a healthy weight. This may be the case, but experimental evidence shows that reducing carbs and increasing fat is more effective for weight loss [7, 8]. In one study, when experiment participants added carbs back into their diet (the increase in calories from 2 months to 12 months is entirely accounted for–and then some–by carbohydrate), they regained the weight they had lost.*

Data from [7]

Kenneth Rothman, in his book Epidemiology: An Introduction, emphasizes the importance of applying Karl Popper’s philosophy of refutationism to epidemiology:

“The refutationist philosophy postulates that all scientific knowledge is tentative in that it may one day need to be refined or even discarded. Under this philosophy, what we call scientific knowledge is a body of as yet unrefuted hypotheses that appear to explain existing observations.” [9]

Rothman makes the point that there is an asymmetry when it comes to refuting hypotheses based on observations: a single contrary observation carries more weight in judging whether or not a hypothesis is false than a hundred observations that suggest that it is true.

If the current nutrition paradigm needs to be “refined or even discarded,” how will we acquire the knowledge we need to create a better system? How can we move away from “statistical people” towards a perspective that encompasses the individual variations in genetics, culture, and lifestyle that have such a tremendous impact on health?

Tune in next time for the final episode of N of 1 nutrition when I ask the all-important question: What the heck does n of 1 nutrition have to do with public health?

*This doesn’t mean that carbs are evil–some of my best friends are carbs–but that the conditions in a population that are associated with a healthy weight and the conditions in an experiment to that lead to increased weight loss are very different.

I spent a lot of time with him earlier this year—okay, really just his book, but his book is so sweet and personal that I felt just like I was sitting at the master’s feet—which were clad in my imagination in the most sensible of shoes—as he unfolded for me the saga of nutritional epidemiology.

What I’m about to say is said with all due respect to the man himself (he’s basically created a whole freekin’ discipline for goodness sake). This is simply my reading of a particular text located within a particular context, i.e. this is what happens when they let English majors into science programs.

There are many reasons why nutritional epidemiology may not be up to the task of giving us a sound basis for nutrition policy. But why take my word for it? If you want to understand the heart of nutritional epidemiology—the driving force behind our bold 40-year march in the misguided direction of one-size-fits-all dietary recommendations—you must read Walter Willett’s Nutritional Epidemiology. It is a book I love more every time I read it, and I say this in all sincerity.

The exciting cover graphics merely hint at the fabulousness that awaits inside!

While I suppose it was written as a sort of textbook, and it is certainly used as one, it doesn’t really read like a textbook. It is part apology and part defense, and is much more about “why” than “how.” And the “why?” that it tries to answer to is “Why apply the techniques of epidemiology to nutrition and chronic disease?”

In this regard, it is a touching masterpiece. Walter Willett, MD, DrPH is a professor at the Harvard School of Public Health and at Harvard Medical School. He is considered by many to be the father of nutritional epidemiology. To stretch the analogy, you can think of nutritional epidemiology as his child. Reading the book this way, it almost moves me to tears (again, not joking*), for I find this book to be a father’s sweet and sad paean to a beautiful prince full of promise, who has grown into a spoiled, churlish, and lazy adult, unfit to rule the kingdom, but with too much of the dreams of many poured into him to banish altogether. And the dreams of the father are the most poignant of all.

Apparently, to Willett’s eternal dismay, the whole field got started off on the wrong foot by focusing on dietary cholesterol (as a cause) and serum cholesterol (as an outcome), associations—as we now know—that turned out to be weak, inconsistent, nonexistent, or even the inverse of what was expected (pp. 5-6, 417-418) . We now know that sub-fractions of serum cholesterol affect heart disease risk differently (LDL-C vs HDL-C, for instance) and that different foods affect different aspects of serum cholesterol differently, making the relationship to overall heart disease risk even more obscure, which seems to be par for the course in this field, as Willett readily admits.

Here, according to Willett, is what we don’t know and can’t do in nutritional epidemiology:

We don’t know any given individual’s true intake. It can only be estimated with greater or lesser degrees of error. (p. 65)

We don’t know any given individual’s true status for a nutrient. Ditto above. (p. 174)

We don’t know the true nutrient content of any given food that a person might eat. Double ditto. (pp. 23-24)

We don’t know what factors/nutrients in a food may operate together to prevent/cause disease. Similarly, we don’t how foods commonly found together in dietary patterns may operate together to prevent/cause disease. (pp. 15, 21-22, 327-328)

We can’t separate metabolic consequences of food intake patterns from the food itself, i.e. what we are looking at in any given data set is really metabolism of food, not food. (p. 15)

We don’t know what really causes the chronic diseases we study in nutrition epidemiology (p. 12); age, genetics, education, income, and lifestyle factors may influence, modify, or be more important than any dietary factor in the origins of these diseases (pp. 10, 15).

We can’t distinguish between causal and coincidental associations. Furthermore, weak associations could be causal; strong associations can be coincidental (p. 12).

Associations we do find are likely to be weak; we will often find no associations at all. Even if we do find statistically significant associations between nutrients and disease, they may be clinically or practically irrelevant and should not necessarily be used to make public health recommendations. (pp. 12-14, 21).

But wait! Willett cries. Don’t give up! This book is also a defense of those shortcomings—although one blinkered by what I must assume is Willett’s love for the field. I am always a little touched and frustrated by the section on why we find so many instances of lack of association between an ostensible nutritional cause and a disease outcome in nutrition epidemiology. Willett meticulously lists the possible reasons one by one as to why we may not be able to “observe a statistically significant association when such an association truly exists” (pp. 12-14). At no time does he venture to offer up the possibility that perhaps—and how would we know one way or the other?—no such association does truly exist.

A new edition of the book is coming out; this should make the old edition cheap in comparison. I won’t read the new edition because I’m afraid it would ruin my romance with the old edition, which is the one I recommend to you.

If you think Gary Taubes is “a poisonous pea in an ideological pod” (as I’ve heard him called), read this book (especially Ch 17 on “Diet and Coronary Heart Disease”). On the other hand, if you think population studies investigating nutrition and chronic disease are basically a gigantic undifferentiated crock of malarkey, read this book. Why? Because there are no clear answers and no real heroes. If you want to know the strengths and weakness of nutritional epidemiology, best to hear them outlined in excruciating and loving detail by Willett himself.

You don’t have to read it cover to cover. Skip around. You’ll learn in passing some methodology behind the folly of trying to forge links between specific nutrients in food to long-term chronic diseases that have multiple and complex origins (just the sections on how we collect information about what we think people are eating are eye-opening in that regard—Ch. 4-8). But I think (I hope) you’ll also hear the voice of a father wise enough to know that children are—must be—brought into this world on grand faith, one that hopes that they will make the world a better place than before, and that his child—nutritional epidemiology—is no different. Willett believes in this child and the book is a statement of that faith.

Please draw your own conclusions, here’s mine: Faith is not science.

Any parent out there knows this: you seem at first to have a child of your own, but you end up sending an adult out into the world who is no longer yours and never really was. The mistakes, limitations, failures, shortcomings belong only to that grown child, not to the parent. But still. It may be hard to acknowledge the fact that your precious one is no better than the other kids and probably won’t save the world. Sometimes, when I’m reading this book—when I’m supposedly studying for an exam—I am caught unawares by the sighs of disappointment, the rally of excuses, and finally the prickly justifications: The prince must be allowed to rule; the king knows he’s a weak little louse, but he’s all we’ve got.

I know—and any of us who are students of literature know—that this is the king’s tragic flaw. The prince can’t save the kingdom; the empire must crumble. But here is the king, holding brick and mortar together through sheer force of will, somehow acknowledging and somehow—at the same time—unaware, that this particular castle was built on sand in the first place. In this book, I hear Willett’s love for a hopelessly flawed field, a touching declaration of blind optimism, and I love this book, and I deeply respect the man himself, for showing that to me.